caa a22 4500 606228756 CHVBK 20210128101205.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s10796-015-9563-4 doi (NATIONALLICENCE)springer-10.1007/s10796-015-9563-4 Semantics-based event log aggregation for process mining and analytics [Elektronische Daten] [Amit Deokar, Jie Tao] In highly complex and flexible environments, event logs tend to exhibit high levels of heterogeneity, and clustering-based methods are candidate techniques for simplifying the mined process models from the process observations. To compensate for the information loss occurring during clustering, semantic information from event logs may be extracted and organized in the form of knowledge structures such as process ontologies using methods of ontology learning. In this article, we propose an overall computational framework for event log pre-processing, and then focus on a specific component of the framework, namely event log aggregation. We develop a detailed system architecture for this component, along with an implemented and evaluated research prototype SemAgg. We use phrase-based semantic similarity between normalized event names to aggregate event logs in a hierarchical form. We discuss the practical implications of this work for learning lower level process ontology classes as well as performing further process mining and analytics. Springer Science+Business Media New York, 2015 Process mining nationallicence Process analytics nationallicence Event logs nationallicence Natural language processing nationallicence Process ontologies nationallicence Agglomerative hierarchical clustering nationallicence Deokar Amit Sam and Irene Black School of Business, Pennsylvania State University, 5101 Jordan Road, Burke Center 268, 16563, Erie, PA, USA aut Tao Jie Information Systems and Operations Management (IS&OM) Department, Dolan School of Business, Fairfield University, 1073N. Benson Road, 06824, Fairfield, CT, USA aut Information Systems Frontiers Springer US; http://www.springer-ny.com 17/6(2015-12-01), 1209-1226 1387-3326 17:6<1209 2015 17 10796 https://doi.org/10.1007/s10796-015-9563-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10796-015-9563-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Deokar Amit Sam and Irene Black School of Business, Pennsylvania State University, 5101 Jordan Road, Burke Center 268, 16563, Erie, PA, USA aut NATIONALLICENCE 700 1- Tao Jie Information Systems and Operations Management (IS&OM) Department, Dolan School of Business, Fairfield University, 1073N. Benson Road, 06824, Fairfield, CT, USA aut NATIONALLICENCE 773 0- Information Systems Frontiers Springer US; http://www.springer-ny.com 17/6(2015-12-01), 1209-1226 1387-3326 17:6<1209 2015 17 10796